Ensemble-based data assimilation methods have shown amazing skill in reducing initial condition errors for numerical weather prediction. However, challenges still remain for basic ensemble Kalman filter algorithms to perform optimally when applied to highly nonlinear dynamical systems with complex relations between observations and states. Recently, many research groups have made progress in advancing methodologies for scenarios such as nonlinear and multi-scale dynamical systems, deviation from Gaussian distribution for state variables, and complex observation operators. The shear complexity of data assimilation and modeling systems often limits the communication and collaboration among researchers with different scientific foci. Yet, inter-disciplinary collaboration is an important step in cracking the nonlinear data assimilation problem. In this session, we welcome researchers to showcase their new findings in developing ensemble filtering methods that provide better results than basic ensemble Kalman filters for problems with large dimensions and strong nonlinearity. We also encourage authors that participate in this session to provide their insights on what should be the commonly adopted benchmark test cases that reflect the current data assimilation challenges. We envision that the discussion stemming from this session will contribute to wider collaboration among researchers to push forward the frontiers of data assimilation science.

Atmospheric profiling is an important tool for understanding the nature and interactions of geophysical phenomena. Within the Earth system framework, representing the vertical structure of our atmosphere is crucial to understand the exchange of energy, momentum, and mass between the atmosphere and the surface (land, ocean, and ice), because these processes have direct implications in air quality, convection, severe weather outbreaks, and climate change. Yet, remote measurements of vertical parameters (e.g., temperature, moisture, wind, traces gases and aerosols) from satellite platforms remain a challenge, due to coarse vertical sampling, limitations in simulating surface and atmospheric processes, and satellite orbital constraints, particularly when it comes down to the characterization of the lower atmosphere where we live.  As a consequence, improvements in the vertical characterization of the Earth-Atmosphere system are deemed urgent and critical for the next decade.

This session will focus on discussing both current research and operational achievements, as well as future directions of satellite vertical profiling methodology (UV/VIS/IR/MW/GPSRO), and their contributions to improve our understanding of thermodynamics, convection, severe weather, air quality, and climate change. Submissions are welcomed to highlight multi-agency (NASA, NOAA, NRL, and Academia) efforts to retrieve and correct passive and active sensors vertical profiling, as well as explore the synergistic relationship between in-situ observations, remote sensing, and weather and climate modeling. We solicit topics of interest that include:

1. Historical perspective of satellite sounders and advancements over the last 20-years.

2. Experiments that explore improvements in vertical sampling

3. Quantification of retrieval uncertainty

4. Impact on satellite-based vertical retrievals on numerical weather prediction

5. BL probing, and impact of PBL processes on weather and air quality through high vertical and temporal profiling of PBL temperature, moisture, and heights

6. Aerosol, cloud and trace gases vertical retrievals and their effects on climate, air quality and radiative balance

7. Sounder retrieved vertical motion of deep convection, heavy precipitation and clouds

8. Sounder retrievals to better understand the vertical structure of hydrometeors

9. Techniques to improve the vertical resolution of water vapor profiling

10. Next generation sensors and nano-satellites

This session will be co-chaired by:

Primary: Mayra I. Oyola (Mayra.I.Oyola@jpl.nasa.gov)

Eric Fetzer, JPL (Eric.J.Fetzer@jpl.nasa.gov)

Nicholas Nalli, NOAA/NESDIS (nick.nalli@noaa.gov)

Chi Ao, JPL (Chi.O.Ao@jpl.nasa.gov)

Panagiotis Vergados, JPL (Panagiotis.Vergados@jpl.nasa.gov)

Description: All observing and assimilation systems contain a wide-variety of parameters.  These parameters could control the quality of retrievals from observing systems.  They might be used to represent background/observation error variances or even the structure of ensemble localization/inflation in assimilation systems.  In all cases these parameters are typically estimated from training sets using statistical methods.  This session intends to provide a broad overview of both theory and practice in the application of statistical parameter estimation for integrated observing and assimilation systems for the atmosphere, oceans, and land surface.  Subject areas of particular interest include, but are not limited to, the following: statistical estimation methods for observation operator development; estimation methods for observation and background error covariance matrices (e.g. Desroziers, Höllingsworth-Lonnberg, etc.); estimation methods for prior/posterior inflation as well as ensemble covariance localization parameters; as well as the application of artificial intelligence/machine learning methods to the tuning/configuration/diagnosis of observing and assimilation systems.

Use of satellite and ground based remote sensing observations has become more and more important in atmospheric and environmental research. Numerous sensors have been developed to retrieve atmospheric compositions including aerosols and trace gases, providing unprecedented opportunities to improve climate and air quality modeling and forecasting. On one hand, observations by different sensors can be joined to reveal a more complete picture of the composition of the atmosphere. For example, MODIS and CALIPSO measurements together form a 3D view of atmospheric aerosols. Observations of aerosol precursor such as SO₂ and NO₂ can provide insights into the causes of aerosol variability. Many research works have been conducted to understand the formation and evolution of air pollution using multiple observational datasets. On the other hand, because different sensors have different optical design, sampling frequency, and spatial/temporal resolution characteristics, it is necessary to explore the integrated usage of these multi-sensor data in order to maximize their capabilities. For example, satellite can provide spatial variability of atmospheric component, whereas ground based sensors typically have higher accuracy and temporal resolution. These two types of observations can thus be combined to for more accurate information with high spatial and temporal coverage. Many data synergy or data fusion techniques have been developed for this purpose. Considering the importance of combining multiple datasets in climate and environmental research, it is quite appropriate and necessary to hold a session about this topic at the 100^th AMS annual meeting, in order to better demonstrate recent progresses and to promote collaborate. This session will cover works including the validation/cross validation of multi-sensor data, developments of data fusion techniques, joint retrieval of atmospheric composition (including aerosols and trace gases) using multiple satellite and ground based platforms, joint application of multi-sensor data in climate and environmental problems and the assimilation of multi-sensor data in weather, climate and air quality modeling and forecasting.